Describe the problem.
It solves — and remembers.
NightShift is an autonomous AI problem solver with a persistent memory. Every run feeds its Knowledge Base. Run 50 is fundamentally faster and cheaper than run 1.
title: SWOT analysis for EV conversion startup description: | Research the market for converting ICE cars to electric. Find real market data. acceptance: - Real citations, not summaries - Executive summary included max_budget_usd: 3.0
# run it nightshift solve problem.yaml # watch live nightshift status # hint mid-run nightshift inject "try a different approach" ✓ Run complete · Quality: 4/5 · Cost: $0.42 ✓ 12 KB entries written · patterns updated
Every AI tool today
is stateless.
Devin forgets your codebase. Cursor doesn't remember yesterday's bugs. Every session starts from zero. NightShift breaks this pattern.
| Capability | Devin · Cursor · Copilot | NightShift |
|---|---|---|
| Learns between runs | — forgets everything | ✓ Knowledge Base — LanceDB hybrid search, 3ms |
| Strategy from experience | — same prompts, same results | ✓ AR patterns — UCB1 scoring, mutate & evolve |
| Non-code tasks | — coding only | ✓ Any task — research, analysis, SWOT, design |
| Smart retry on failure | — full restart | ✓ Blame-driven — only failed agents re-run |
| User input mid-run | — must restart | ✓ Auditor inbox — inject hints anytime |
| Self-evaluation | — basic or none | ✓ Sub-evaluators — spawn specialists per dimension |
| Exploration drive | — safe-play loops forever | ✓ Investor — prevents repetitive failure patterns |
Watch the team work
Five specialist agents coordinate on every run. See how they collaborate, monitor, evaluate, and learn — all from a single problem description.
Nine pillars.
One engine.
Every feature flows through a single, unified engine. No special code paths. No hidden gates. One architecture that handles every problem type.
Every run produces knowledge: strategies, errors, domain facts. Stored in LanceDB with ModernBERT embeddings. Two tiers: global (~/.nightshift/kb) and project-local. Hybrid BM25 + vector search, 3ms.
Team patterns with UCB1 scoring — the same math as Monte Carlo tree search. Proven patterns get exploited, untried patterns get an exploration bonus. Patterns mutate, compete, and evolve.
Auditor watches every event — failures, cost burn, repeated errors. Investor reads the full picture and pushes exploration signals: explore, exploit, deliver, reboot. Together they prevent safe-play loops.
Scores output quality (1–5) and approach sanity. Spawns sub-evaluators for complex output. Git diff is the source of truth — not stale file state. Quality 0 means FAIL, not "needs improvement."
Every strategy — pipeline, iterations, decomposition, sub-problems —
runs through one function: _solve_pipeline.
13 features integrated: KB, simulation, auditor, blame, checkpoints, cost tracking…
Files are the API. status.json has full state.
events.jsonl is the event stream.
inbox.jsonl is the mailbox.
Any tool integrates — no server, no WebSocket, no API contract.
Episodic memory scores strategies with UCB1. KB captures evaluator reviews, node failures, investor valuations. Predictor queries KB before each node runs to flag known risks. Librarian consolidates after each run.
Without exploration pressure, any system defaults to what worked before — even when it's failing. High uncertainty → high risk appetite → bold moves. UCB exploration bonus at the pattern level ensures novelty.
User messages flow through the same path as Investor signals — into the Auditor inbox, delivered at next replan. The system doesn't distinguish human hints from algorithmic pressure. Both are just signals.
Every agent reads & writes
the same shared memory.
Run 50 is different
from run 1.
As the Knowledge Base grows, every metric improves. NightShift doesn't just get better at your specific problem — it gets better at the entire class of problems you run.
KB grows with every run
Successful strategies, error patterns, domain facts — all indexed with hybrid search. The Coordinator reads relevant history before planning.
AR evolves better team patterns
Patterns with high quality scores get proposed more often. Patterns that fail repeatedly get mutated. The gene pool improves.
Predictor flags known risks
Before each node runs, the Predictor queries KB for past failures on similar nodes. You don't repeat the same mistakes.
Librarian keeps KB clean
After each run, Librarian (Claude Haiku) consolidates — merges duplicates, drops noise, keeps actionable insights. Signal stays high.
Pay for what
you actually use.
Self-host for free, or use the cloud with usage-based billing. No seat licenses. No arbitrary rate limits. You pay when knowledge is created.
- Full 9-pillar architecture
- Local LanceDB knowledge base
- Unlimited runs on your hardware
- CLI tools (solve, run, status, inject)
- File-based monitoring API
- Community support
- Everything in Self-Hosted
- Managed LanceDB (no setup)
- Cross-project Knowledge Base
- Automatic Librarian consolidation
- Run history & analytics
- Priority support
- Free tier: 50 runs / 500 KB queries
- Everything in Pro
- Shared team Knowledge Base
- Private deployment (your infra)
- SSO / SAML
- Custom budget controls
- SLA + dedicated support
- Volume pricing
How usage-based pricing works: KB queries happen when agents search the Knowledge Base (typically 2–8 per run). Runs are billed on completion. A typical $3 problem YAML produces $0.20–$0.60 in platform cost on top of your LLM API usage.
Start solving problems
that remember what worked.
Join developers using NightShift to solve complex problems autonomously — and watch performance compound across every run.